196 research outputs found
CORE: Cooperative Reconstruction for Multi-Agent Perception
This paper presents CORE, a conceptually simple, effective and
communication-efficient model for multi-agent cooperative perception. It
addresses the task from a novel perspective of cooperative reconstruction,
based on two key insights: 1) cooperating agents together provide a more
holistic observation of the environment, and 2) the holistic observation can
serve as valuable supervision to explicitly guide the model learning how to
reconstruct the ideal observation based on collaboration. CORE instantiates the
idea with three major components: a compressor for each agent to create more
compact feature representation for efficient broadcasting, a lightweight
attentive collaboration component for cross-agent message aggregation, and a
reconstruction module to reconstruct the observation based on aggregated
feature representations. This learning-to-reconstruct idea is task-agnostic,
and offers clear and reasonable supervision to inspire more effective
collaboration, eventually promoting perception tasks. We validate CORE on
OPV2V, a large-scale multi-agent percetion dataset, in two tasks, i.e., 3D
object detection and semantic segmentation. Results demonstrate that the model
achieves state-of-the-art performance on both tasks, and is more
communication-efficient.Comment: Accepted to ICCV 2023; Code: https://github.com/zllxot/COR
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction
for developing a new generation of intelligent transportation systems.
Collaborative perception (CP) as an essential component to achieve V2X can
overcome the inherent limitations of individual perception, including occlusion
and long-range perception. In this survey, we provide a comprehensive review of
CP methods for V2X scenarios, bringing a profound and in-depth understanding to
the community. Specifically, we first introduce the architecture and workflow
of typical V2X systems, which affords a broader perspective to understand the
entire V2X system and the role of CP within it. Then, we thoroughly summarize
and analyze existing V2X perception datasets and CP methods. Particularly, we
introduce numerous CP methods from various crucial perspectives, including
collaboration stages, roadside sensors placement, latency compensation,
performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover,
we conduct extensive experimental analyses to compare and examine current CP
methods, revealing some essential and unexplored insights. Specifically, we
analyze the performance changes of different methods under different
bandwidths, providing a deep insight into the performance-bandwidth trade-off
issue. Also, we examine methods under different LiDAR ranges. To study the
model robustness, we further investigate the effects of various simulated
real-world noises on the performance of different CP methods, covering
communication latency, lossy communication, localization errors, and mixed
noises. In addition, we look into the sim-to-real generalization ability of
existing CP methods. At last, we thoroughly discuss issues and challenges,
highlighting promising directions for future efforts. Our codes for
experimental analysis will be public at
https://github.com/memberRE/Collaborative-Perception.Comment: 19 page
LiDAR aided simulation pipeline for wireless communication in vehicular traffic scenarios
Abstract. Integrated Sensing and Communication (ISAC) is a modern technology under development for Sixth Generation (6G) systems. This thesis focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a Light Detection and Ranging (LiDAR) based system. The simulation pipeline can be used to generate data sets for numerous problems. Additionally, the developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. LiDAR behavior on traffic environment is provided as part of the results in this study. A periodic beam index map is developed by capturing antenna azimuth and elevation angles, which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that are needed to be sent in Vehicle to Infrastructure (V2I) communication. This approach effectively reduces the wireless communication overhead in V2I communication
A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%
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